Hey, my name is Dan and I wear multiple hats (for better or worse). On the one hand, I am an applied researcher in artificial intelligence and machine learning, focusing on efficient and robust learning in NLP, computer vision, and within deep learning optimization in general. Some examples of areas I'm currently focusing on include: efficient multi-modal learning, sustainable and green A.I. with a focus on mitigating the compute/carbon costs of deep learning workloads, high-performance computing (HPC), interactions between large language models (LLMs) and foundation models as agents, and distillation/interpretability of LLMs. On the other hand, I am also an economist and data scientist with a prior focus on macroeconomics, causal inference, and behavioral economics. Having worked in public policy, international trade, finance, big tech, quantum computing, social media, healthcare/biotech, drug discovery, and more, I try to take an interdisciplinary (read: unconventional) approach in my research, work, and leadership style. As an applied AI scientist, economist, and data scientist all-in-one, I have 9+ years of experience across industry, academia, and government. Though much of my previous/current work has been more applied--i.e., designing, prototyping, building, and scaling in-house systems, models, and algorithms--I also (somehow) have publications ranging from machine learning conferences such as NeurIPS to engineering or distributed computing venues like IEEE and ACM as well as economic journals like Empirical Economics, etc. Some other parts of my work experience have also involved biz ops, biz dev, product, etc. I've also had the honor of advising some really cool start-ups and mentoring some very bright students.With extensive experience as an individual contributor and team lead, as well as building/cultivating teams from the ground up, I have made contributions to ML‑powered systems that are still in use today from models/algorithms powering pricing engines to efficiently guiding deep learning training jobs, etc.Most importantly--I'm still learning as I go along.